Energy storage and charging regulation and control system adaptive to photovoltaic equipment

Abstract

The invention belongs to the technical field of photovoltaic equipment management and control, and particularly relates to an energy storage charging regulation and control system adaptive to photovoltaic equipment, which comprises a comprehensive monitoring output module, a regulation and control strategy analysis generation module, a regulation and control strategy execution control module, an energy storage safety influence analysis module and a background management end, according to the invention, the comprehensive monitoring output module monitors the operation of the photovoltaic device, the energy storage device and the charging device, the regulation and control strategy analysis and generation module processes and analyzes the monitoring data and generates an optimal energy storage and charging regulation and control strategy, and the regulation and control strategy execution control module controls the operation process of the energy storage device based on the regulation and control strategy. The stability of the photovoltaic power generation system is enhanced, energy storage potential safety hazards of the energy storage device are analyzed through the energy storage safety influence analysis module, reason investigation and analysis are carried out when energy storage safety high influence signals are generated, reasonable improvement measures are taken, safe and stable operation of the energy storage device is guaranteed, and the intelligent level and the automation level are high.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic equipment control, and in particular to an energy storage charging control system adapted for photovoltaic equipment. Background Art

[0002] Photovoltaic equipment refers to equipment that uses the photovoltaic effect to convert solar energy into electrical energy. It is widely used in households, businesses, industries and other fields. It is an important means to achieve clean energy utilization. With the increasing attention paid to renewable energy, photovoltaic power generation, as an important part of new energy, has been continuously expanded in scale of application.

[0003] Currently, it is difficult to effectively manage the energy storage charging of photovoltaic equipment, and it is impossible to reasonably analyze the energy storage risk and energy storage stability of the corresponding energy storage devices and accurately evaluate the difficulty of controlling all energy storage devices. This is not conducive to achieving effective control of energy storage devices and ensuring their safe and stable operation, and the level of intelligence and automation is low;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an energy storage charging control system suitable for photovoltaic equipment, which solves the problems that the prior art is difficult to effectively manage the energy storage charging of photovoltaic equipment, and is unable to reasonably analyze the energy storage risk and energy storage stability of the corresponding energy storage devices and accurately evaluate the difficulty of control for all energy storage devices, and has a low level of intelligence and automation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The energy storage charging control system adapted to photovoltaic equipment includes a comprehensive monitoring output module, a control strategy analysis and generation module, a control strategy execution control module, an energy storage safety impact analysis module and a background management terminal; the comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices and charging devices, and sends all monitoring data to the control strategy analysis and generation module;

[0008] The control strategy analysis and generation module uses machine learning algorithms or deep learning algorithms to process and analyze the collected monitoring data, predict the power generation and electricity demand in the future, automatically generate the optimal energy storage and charging control strategy, and send the generated control strategy to the control strategy execution control module and the background management end;

[0009] The regulation strategy execution control module controls the operation process of the energy storage device based on the regulation strategy. The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, generates a high energy storage safety impact signal or a low energy storage safety impact signal through analysis, and sends the high energy storage safety impact signal to the background management end; when the background management end receives the high energy storage safety impact signal, it will issue a corresponding warning.

[0010] Furthermore, the photovoltaic device converts light energy into electrical energy and outputs it to an energy storage device or a power grid; the energy storage device is used to store the electrical energy generated by the photovoltaic device and release the electrical energy for use by the charging device or the power grid when needed, and the energy storage device is a lithium-ion battery pack; the charging device is used to convert the electrical energy in the energy storage device into electrical energy suitable for use by electric vehicles or other electrical equipment and charge them, and the charging device has multiple charging modes, including constant current charging, constant voltage charging and pulse charging.

[0011] Furthermore, the specific analysis process of the energy storage safety impact analysis module includes:

[0012] Several detection periods are set within a unit of time, and the actual power consumption value and the actual power output value of the energy storage device within the corresponding detection period are collected. The difference between the actual power consumption value and the actual power output value is calculated and the difference result is marked as the energy storage abnormal loss value. The energy storage abnormal loss value is numerically compared with the preset energy storage abnormal loss threshold. If the energy storage abnormal loss value exceeds the preset energy storage abnormal loss threshold, the corresponding detection period is marked as an energy storage risk period;

[0013] The number of energy storage risk time periods in a unit time is obtained and the ratio is calculated with the number of detection time periods to obtain the energy storage risk time condition value, and the energy storage abnormal loss values ​​of all detection time periods in a unit time are averaged to obtain the energy storage abnormal table value, and the energy storage abnormal loss value with the largest value in a unit time is marked as the energy storage abnormal amplitude value; the energy storage leakage assessment value is obtained by numerically calculating the energy storage risk time condition value, the energy storage abnormal table value and the energy storage abnormal amplitude value, and the energy storage leakage assessment value is numerically compared with the preset energy storage leakage assessment threshold. If the energy storage leakage assessment value exceeds the preset energy storage leakage assessment threshold, a high impact signal for energy storage safety is generated.

[0014] Furthermore, if the energy storage leakage assessment value does not exceed the preset energy storage leakage assessment threshold, the temperature data of several positions in the energy storage device are collected, and the average value of the temperature data of all positions is marked as the energy storage internal temperature value. A rectangular coordinate system is established with time as the X-axis and the energy storage internal temperature value as the Y-axis, and a change curve of the energy storage internal temperature value is drawn in the first quadrant and marked as a gradual change curve, and the starting point of the gradual change curve is located on the Y-axis;

[0015] In the first quadrant, draw a ray parallel to the X-axis and with its endpoint on the Y-axis. If the end point of the gradient curve is above the ray, connect the end point of the gradient curve with the ray through a vertical line segment. Mark the part of the gradient curve above the ray and the area enclosed by the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to obtain the temperature warning performance value.

[0016] The average value of the vibration data of the energy storage device in a unit time is marked as the vibration analysis value, and the average value of the decibel value of the noise generated by the energy storage device in a unit time is marked as the noise analysis value. The energy storage safety impact coefficient is obtained by numerically calculating the temperature alarm performance value, the vibration analysis value and the noise analysis value, and the energy storage safety impact coefficient is numerically compared with the preset energy storage safety impact coefficient threshold value;

[0017] If the energy storage safety impact coefficient exceeds the preset energy storage safety impact coefficient threshold, a high energy storage safety impact signal is generated; if the energy storage safety impact coefficient does not exceed the preset energy storage safety impact coefficient threshold, a low energy storage safety impact signal is generated.

[0018] Furthermore, the energy storage safety impact analysis module is communicatively connected to the energy storage stability analysis module. The energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module. The energy storage stability analysis module analyzes the energy storage stability status of the energy storage device during the detection period, generates an energy storage high stability signal or an energy storage low stability signal through analysis, and sends the energy storage high stability signal or the energy storage low stability signal to the background management end. When the background management end receives the energy storage low stability signal, it issues a corresponding warning.

[0019] Furthermore, the specific analysis process of the energy storage stability analysis module includes:

[0020] The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage danger frequency value, and the generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage danger meter time, and the interval between two adjacent groups of energy storage danger meter times is marked as the danger interval value, and the danger interval value is numerically compared with the preset danger interval threshold value. If the danger interval value does not exceed the preset danger interval threshold value, the corresponding danger interval value is marked as the danger interval value;

[0021] The number of dangerous situation isolation values ​​within the detection period is obtained and marked as dangerous situation isolation values, and the energy storage dangerous frequency value and the dangerous situation isolation value are weighted and summed to obtain the energy storage stability influence coefficient, and the energy storage stability influence coefficient is numerically compared with the preset energy storage stability influence coefficient threshold. If the energy storage stability influence coefficient exceeds the preset energy storage stability influence coefficient threshold, an energy storage low stability signal is generated; if the energy storage stability influence coefficient does not exceed the preset energy storage stability influence coefficient threshold, an energy storage high stability signal is generated.

[0022] Furthermore, the energy storage stability analysis module is communicatively connected to the energy storage control analysis module. The energy storage stability analysis module sends the energy storage high stability signal or the energy storage low stability signal of the corresponding energy storage device to the energy storage management analysis module. The energy storage management analysis module obtains all energy storage devices that need to be managed, and analyzes the difficulty of control of all energy storage devices. Through the analysis, it is determined whether to generate an energy storage control enhancement signal, and the energy storage control enhancement signal is sent to the background management end. When the background management end receives the energy storage control enhancement signal, it issues a corresponding warning.

[0023] Furthermore, the specific process of analyzing and determining whether to generate an energy storage management enhancement signal is as follows:

[0024] If the corresponding energy storage device corresponds to the low-stable energy storage signal, the corresponding energy storage device is marked as an abnormal storage device; if the corresponding energy storage device corresponds to the high-stable energy storage signal, the energy storage tracking value of the corresponding energy storage device is obtained through energy storage device tracking analysis, and the energy storage tracking value is numerically compared with the preset energy storage tracking threshold. If the energy storage tracking value exceeds the preset energy storage tracking threshold, the corresponding energy storage device is marked as an abnormal storage device;

[0025] The number of energy storage devices is obtained and the ratio is calculated with the total number of energy storage devices that need to be supervised to obtain the energy storage detection value, and the energy storage detection value is compared with the preset energy storage detection threshold. If the energy storage detection value exceeds the preset energy storage detection threshold, an energy storage control enhancement signal is generated.

[0026] Furthermore, the specific analysis process of energy storage device tracking analysis is as follows:

[0027] The production date of the corresponding energy storage device is collected, and the interval between the current date and the production date is marked as the energy storage duration, and the power storage performance degradation of the corresponding energy storage device is collected and marked as storage performance degradation;

[0028] and collecting the total time that the corresponding energy storage device is in an overcharge state and the total time that it is in an over-discharge state, and summing the total time that it is in the overcharge state and the total time that it is in the over-discharge state to obtain a charge and discharge abnormality value;

[0029] Real-time detection data of various environmental parameters of the environment in which the corresponding energy storage device is located is collected. If there are environmental parameters whose real-time detection data do not meet the requirements of the corresponding preset data, the corresponding energy storage device is judged to be in an external risk state; the total duration of the corresponding energy storage device in the external risk state in the historical stage is obtained and marked as the external risk time value, and the energy storage tracking value is obtained by numerically calculating the energy storage time, storage degradation, abnormal charging and discharging values, and external risk time value.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. In the present invention, the photovoltaic equipment, energy storage device and charging device are monitored for operation through a comprehensive monitoring output module, the control strategy analysis and generation module processes and analyzes the monitoring data and generates the optimal energy storage and charging control strategy, the control strategy execution control module controls the operation process of the energy storage device based on the control strategy, enhances the stability of the photovoltaic power generation system, and analyzes the energy storage safety hazards of the energy storage device through the energy storage safety impact analysis module, conducts cause investigation and analysis when generating a high impact signal of energy storage safety, and makes reasonable improvement measures, which is conducive to ensuring the safe and stable operation of the energy storage device;

[0032] 2. In the present invention, the energy storage stability status of the energy storage device during the detection period is analyzed through the energy storage stability analysis module, and the operation supervision and control of the corresponding energy storage device is strengthened when the energy storage low stability signal is generated, so as to further ensure the safe and stable operation of the energy storage device. The energy storage management analysis module analyzes the difficulty of control of all energy storage equipment, and makes corresponding control measures adjustment and improvement when generating the energy storage control enhancement signal, which is conducive to improving the subsequent control effect and ensuring the safe and stable operation of the energy storage device, and has a high level of intelligence and automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0034] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0035] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1: Figure 1 As shown, the energy storage charging control system adapted to photovoltaic equipment proposed by the present invention includes a comprehensive monitoring output module, a control strategy analysis and generation module, a control strategy execution control module, an energy storage safety impact analysis module and a background management terminal; the comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices and charging devices, collects data information such as the power generation of photovoltaic equipment, the power status of energy storage devices and the demand of power-consuming equipment in real time, and sends all monitoring data to the control strategy analysis and generation module;

[0038] It should be noted that photovoltaic equipment converts light energy into electrical energy and outputs it to an energy storage device or a power grid; the energy storage device is used to store the electrical energy generated by the photovoltaic equipment and release the electrical energy for use by the charging device or the power grid when needed, and the energy storage device is a lithium-ion battery pack, which has the advantages of high energy density, long cycle life, and no pollution; the charging device is used to convert the electrical energy in the energy storage device into electrical energy suitable for electric vehicles or other electrical equipment and charge them, and the charging device has multiple charging modes, including constant current charging, constant voltage charging, and pulse charging.

[0039] The control strategy analysis and generation module uses machine learning algorithms or deep learning algorithms to process and analyze the collected monitoring data, predict the power generation and electricity demand in the future, automatically generate the optimal energy storage and charging control strategy, and send the generated control strategy to the control strategy execution control module and the background management end. The control strategy execution control module controls the operation process of the energy storage device based on the control strategy, and the management personnel can remotely control the energy storage device through the background management end; the operation process of the control strategy analysis and generation module can be summarized as follows:

[0040] Data collection and preprocessing: Collect monitoring data, such as photovoltaic power generation data: including real-time power generation of photovoltaic panels, solar radiation intensity, temperature, etc.; power demand data: from smart meters, power management systems, etc., recording real-time power consumption and power consumption patterns; energy storage device data: key parameters such as the power of the energy storage device, charging / discharging status, etc.; external data: such as weather forecasts, holiday information, seasonal changes, etc., these data are crucial to predicting future power generation and power demand;

[0041] Preprocessing steps are performed on the collected data, including data cleaning (removing outliers and handling missing values), data standardization (converting data into a unified format and standard), and data normalization (scaling data to the same range) to ensure data quality and consistency;

[0042] Feature extraction and selection: Feature extraction is performed in the preprocessed data, that is, the variables that have an important impact on the prediction of future power generation and electricity demand are identified. These features include historical power generation, historical electricity demand, solar radiation intensity, temperature, etc. Feature selection is another key step, which aims to select the most predictive feature subset from the extracted features to reduce computational complexity and improve prediction accuracy.

[0043] Model training and verification: Use machine learning algorithms or deep learning algorithms to build prediction models. Common machine learning algorithms include linear regression, support vector machine, random forest, etc., while deep learning algorithms may include neural networks, recurrent neural networks (RNN), long short-term memory networks (LSTM), etc.;

[0044] In addition, during the model training phase, historical data will be used to train the model so that it can learn the complex relationship between power generation, power demand and characteristic variables. During the training process, the model parameters will be continuously adjusted to minimize the prediction error. During the model verification phase, a portion of data that was not involved in the training will be used to evaluate the performance of the model. By comparing the model's prediction results with the actual observations, the model's prediction accuracy and generalization ability can be evaluated.

[0045] Predicting future power generation and demand: Once the model is trained and verified, the real-time collected data and the prediction model are used to predict power generation and demand in the future. The prediction results take into account a variety of factors, such as weather changes, holidays, seasonal changes, etc., to ensure the accuracy and reliability of the prediction;

[0046] Automatically generate optimal energy storage and charging control strategies: Based on the predicted future power generation and electricity demand, optimization algorithms (such as dynamic programming, genetic algorithms, etc.) are used to automatically generate optimal energy storage and charging control strategies; these strategies fully consider factors such as the capacity of the energy storage device, charging / discharging efficiency, and grid demand to ensure that the energy storage device can provide sufficient power when needed while avoiding overcharging or discharging;

[0047] Real-time adjustment and optimization: During the actual operation process, new monitoring data is continuously collected, and the prediction model and control strategy are adjusted and optimized in real time based on the real-time data, including updating the parameters of the prediction model, adjusting the feature selection strategy, optimizing the energy storage and charging control strategy, etc., to ensure that the system can adapt to changes in light and fluctuations in electricity demand and maintain stable operation.

[0048] The present invention can realize efficient storage and intelligent distribution of the electric energy generated by photovoltaic equipment, reduce energy waste, improve energy utilization efficiency, enhance system stability, and adjust the charging and discharging strategies of the energy storage device in advance by predicting the power generation of photovoltaic equipment and the power consumption of power-consuming equipment in the future, realize the balance and optimal utilization of electric energy, enhance the stability of the photovoltaic power generation system, and select different charging modes and powers according to the type and demand of power-consuming equipment, improve charging efficiency, shorten charging time, and have a high level of intelligence and automation.

[0049] The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, generates a high energy storage safety impact signal or a low energy storage safety impact signal through analysis, and sends the high energy storage safety impact signal to the background management end. When the background management end receives the high energy storage safety impact signal, it issues a corresponding warning to remind the management personnel to conduct cause investigation and analysis in time and make reasonable improvement measures to reduce the operation risk of the energy storage device, thereby ensuring the safe and stable operation of the energy storage device; the specific analysis process of the energy storage safety impact analysis module is as follows:

[0050] Several detection periods are set within a unit time, and the duration of all detection periods is the same; the actual power consumption value and the actual power output value of the energy storage device within the corresponding detection period are collected, and the difference between the actual power consumption value (i.e., the power consumed by the energy storage device) and the actual power output value (i.e., the power grid and the power-consuming equipment received from the corresponding energy storage device) is calculated and the difference result is marked as the energy storage abnormal loss value;

[0051] It should be noted that the larger the value of the energy storage abnormal loss value, the greater the possibility of leakage of the energy storage device in the corresponding detection period; the energy storage abnormal loss value is compared with the preset energy storage abnormal loss threshold value. If the energy storage abnormal loss value exceeds the preset energy storage abnormal loss threshold value, it indicates that there is a leakage risk in the corresponding detection period, and the corresponding detection period is marked as an energy storage risk period;

[0052] The number of energy storage risk periods per unit time is obtained and the ratio is calculated with the number of detection periods to obtain the energy storage risk status value, and the energy storage abnormal loss values ​​of all detection periods per unit time are averaged to obtain the energy storage abnormal value, and the energy storage abnormal loss value with the largest value per unit time is marked as the energy storage abnormal amplitude value;

[0053] The energy storage leakage assessment value XL is obtained by numerically calculating the energy storage risk condition value WY, the energy storage abnormal table value ZQ and the energy storage abnormal amplitude value SP through the formula XL=eq×WY+uy×ZQ+te×SP, wherein eq, uy and te are preset proportional coefficients with values ​​greater than zero, and the larger the value of the energy storage leakage assessment value XL is, the more serious the leakage hazard of the energy storage device is;

[0054] The energy storage leakage assessment value XL is numerically compared with the preset energy storage leakage assessment threshold. If the energy storage leakage assessment value XL exceeds the preset energy storage leakage assessment threshold, it indicates that the energy storage device has a serious leakage hazard, which is not conducive to the safe operation of the energy storage power supply, and a high energy storage safety impact signal is generated.

[0055] Furthermore, if the energy storage leakage assessment value XL does not exceed the preset energy storage leakage assessment threshold, the temperature data of several positions in the energy storage device are collected, and the average value of the temperature data of all positions is marked as the energy storage internal temperature value. A rectangular coordinate system is established with time as the X-axis and the energy storage internal temperature value as the Y-axis, and a change curve of the energy storage internal temperature value is drawn in the first quadrant and marked as a gradual change curve, and the starting point of the gradual change curve is located on the Y-axis;

[0056] In the first quadrant, draw a ray parallel to the X-axis and with its endpoint on the Y-axis. If the end point of the gradient curve is above the ray, connect the end point of the gradient curve with the ray through a vertical line segment. Mark the part of the gradient curve above the ray and the area enclosed by the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to obtain the temperature warning performance value.

[0057] The average value of the vibration data (i.e., vibration amplitude) of the energy storage device in unit time is marked as the vibration analysis value, and the average value of the noise decibel value generated by the energy storage device in unit time is marked as the noise analysis value. The temperature warning performance value RY, the vibration analysis value KL and the noise analysis value WN are numerically calculated by the formula FM=(a×RY+c×KL+n×WN) / 3 to obtain the energy storage safety impact coefficient FM; wherein a, c, and n are preset weight coefficients with values ​​greater than zero, and the larger the value of the energy storage safety impact coefficient FM, the higher the operating risk of the energy storage device;

[0058] The energy storage safety impact coefficient FM is numerically compared with the preset energy storage safety impact coefficient threshold; if the energy storage safety impact coefficient FM exceeds the preset energy storage safety impact coefficient threshold, it indicates that the operation risk of the energy storage device is relatively high, which is not conducive to the safe operation of the energy storage power supply, and a high energy storage safety impact signal is generated; if the energy storage safety impact coefficient FM does not exceed the preset energy storage safety impact coefficient threshold, it indicates that the operation risk of the energy storage device is relatively low, and a low energy storage safety impact signal is generated.

[0059] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the energy storage safety impact analysis module is communicatively connected to the energy storage stability analysis module, the energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module, and the energy storage stability analysis module analyzes the energy storage stability status of the energy storage device within the detection period (preferably, the detection period is twenty days), and generates an energy storage high stability signal or an energy storage low stability signal through analysis;

[0060] And the energy storage high stability signal or energy storage low stability signal is sent to the background management end. When the background management end receives the energy storage low stability signal, it issues a corresponding warning to remind the management personnel to continue to focus on the operation of the corresponding energy storage device, strengthen the operation supervision and control of the corresponding energy storage device, and facilitate the targeted strengthening of energy storage device management to ensure the safe and stable operation of the energy storage device. The specific analysis process of the energy storage stability analysis module is as follows:

[0061] The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage danger frequency value, and the generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage danger meter time, and the interval between two adjacent groups of energy storage danger meter times is marked as the danger interval value, and the danger interval value is numerically compared with the preset danger interval threshold value. If the danger interval value does not exceed the preset danger interval threshold value, the corresponding danger interval value is marked as the danger interval value;

[0062] The number of dangerous isolation values ​​in the detection period is obtained and marked as dangerous isolation values, and the energy storage stability influence coefficient QW is calculated by weighted summing the energy storage dangerous frequency value HK and the dangerous isolation value NS through the formula QW=bg×HK+us×NS; wherein bg and us are preset weight coefficients with values ​​greater than zero, and the value of us is greater than bg; and the larger the value of the energy storage stability influence coefficient QW, the worse the operating stability of the corresponding energy storage device in the detection period;

[0063] The energy storage stability influence coefficient QW is numerically compared with the preset energy storage stability influence coefficient threshold. If the energy storage stability influence coefficient QW exceeds the preset energy storage stability influence coefficient threshold, it indicates that the operation stability of the corresponding energy storage device during the detection period is poor and supervision needs to be strengthened, and a low energy storage stability signal is generated; if the energy storage stability influence coefficient QW does not exceed the preset energy storage stability influence coefficient threshold, it indicates that the operation stability of the corresponding energy storage device during the detection period is good, and a high energy storage stability signal is generated.

[0064] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the energy storage stability analysis module is communicatively connected to the energy storage control analysis module, the energy storage stability analysis module sends the energy storage high stability signal or the energy storage low stability signal of the corresponding energy storage device to the energy storage management analysis module, the energy storage management analysis module obtains all energy storage devices that need to be managed, analyzes the control difficulty of all energy storage devices, and determines whether to generate an energy storage control enhancement signal through analysis;

[0065] The energy storage management enhancement signal is sent to the background management end. When the background management end receives the energy storage management enhancement signal, it issues a corresponding warning. When the management personnel receive the energy storage management enhancement signal, they make corresponding management and control measures adjustments and improvements, and timely strengthen the subsequent supervision of all energy storage devices, which is conducive to improving the subsequent management and control effect and ensuring the safe and stable operation of the energy storage device. The specific process of judging whether to generate the energy storage management enhancement signal through analysis is as follows:

[0066] If the corresponding energy storage device corresponds to the low energy storage stability signal, it indicates that the operation stability of the corresponding energy storage device is not good and the control is difficult, then the corresponding energy storage device is marked as an abnormal storage device;

[0067] If the corresponding energy storage device corresponds to the energy storage high stability signal, the energy storage tracking value GP of the corresponding energy storage device is obtained through energy storage device tracking analysis, specifically: the production date of the corresponding energy storage device is collected, and the interval between the current date and the production date is marked as the energy storage duration, and the power storage performance degradation of the corresponding energy storage device (that is, the data value of the difference between the actual amount of electricity that can be stored and its theoretical maximum storage amount) is collected and marked as storage degradation;

[0068] and collecting the total time that the corresponding energy storage device is in an overcharge state and the total time that it is in an over-discharge state, and summing the total time that it is in the overcharge state and the total time that it is in the over-discharge state to obtain a charge and discharge abnormality value;

[0069] Collect real-time detection data of various environmental parameters (such as temperature, humidity and other parameters) of the environment in which the corresponding energy storage device is located. If there are environmental parameters whose real-time detection data do not meet the requirements of the corresponding preset data, the corresponding energy storage device is judged to be in an external risk state; obtain the total time that the corresponding energy storage device is in the external risk state in the historical stage and mark it as the external risk time value;

[0070] The energy storage tracking value GP is obtained by numerically calculating the energy storage duration WS, the storage degradation value FX, the abnormal charge and discharge value QL and the external risk time value HY through the formula GP=mu×WS+re×FX+hg×QL+kp×HY; wherein, mu, re, hg, kp are preset weight coefficients with values ​​greater than zero, and the larger the value of the energy storage tracking value GP, the worse the current quality condition of the corresponding energy storage device is, and the more difficult it is to ensure its safe and stable operation;

[0071] Compare the energy storage tracking value GP with the preset energy storage tracking threshold. If the energy storage tracking value GP exceeds the preset energy storage tracking threshold, it indicates that the current quality of the corresponding energy storage device is not good and it is difficult to ensure its continuous, safe and stable operation. Then, the corresponding energy storage device is marked as an abnormal storage device.

[0072] The number of energy storage devices is obtained and the ratio is calculated with the total number of energy storage devices that need to be supervised to obtain the energy storage detection value, and the energy storage detection value is compared with the preset energy storage detection threshold. If the energy storage detection value exceeds the preset energy storage detection threshold, it indicates that the control of all energy storage devices is difficult and the energy storage equipment control needs to be strengthened, then an energy storage control enhancement signal is generated.

[0073] The working principle of the present invention is as follows: when in use, the operation of the photovoltaic equipment, energy storage device and charging device is monitored through the comprehensive monitoring output module, the control strategy analysis and generation module uses a machine learning algorithm or a deep learning algorithm to process and analyze the collected monitoring data, and automatically generates the optimal energy storage and charging control strategy. The control strategy execution control module controls the operation process of the energy storage device based on the control strategy, and can efficiently store and intelligently distribute the electric energy generated by the photovoltaic equipment, improve energy utilization efficiency, achieve balance and optimal utilization of electric energy, and enhance the stability of the photovoltaic power generation system. The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, and when a high-impact signal of energy storage safety is generated, a cause investigation and analysis is conducted and reasonable improvement measures are taken to reduce the operation risk of the energy storage device, which is conducive to ensuring the safe and stable operation of the energy storage device.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An energy storage charging control system adapted to photovoltaic equipment, characterized in that: It includes a comprehensive monitoring output module, a control strategy analysis and generation module, a control strategy execution control module, an energy storage safety impact analysis module and a background management terminal; the comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices and charging devices, and sends all monitoring data to the control strategy analysis and generation module; The control strategy analysis and generation module uses machine learning algorithms or deep learning algorithms to process and analyze the collected monitoring data, predict the power generation and electricity demand in the future, automatically generate the optimal energy storage and charging control strategy, and send the generated control strategy to the control strategy execution control module and the background management end; The regulation strategy execution control module controls the operation process of the energy storage device based on the regulation strategy. The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, generates a high energy storage safety impact signal or a low energy storage safety impact signal through analysis, and sends the high energy storage safety impact signal to the background management end.

2. The energy storage charging control system adapted for photovoltaic equipment according to claim 1 is characterized in that: Photovoltaic equipment converts light energy into electrical energy and outputs it to an energy storage device or a power grid; the energy storage device is used to store the electrical energy generated by the photovoltaic equipment and release the electrical energy for use by the charging device or the power grid when needed, and the energy storage device is a lithium-ion battery pack; the charging device is used to convert the electrical energy in the energy storage device into electrical energy suitable for use by electric vehicles or other electrical equipment and charge them, and the charging device has multiple charging modes, including constant current charging, constant voltage charging and pulse charging.

3. The energy storage charging control system adapted for photovoltaic equipment according to claim 1 is characterized in that: The specific analysis process of the energy storage safety impact analysis module includes: A number of detection periods are set within a unit of time. If the energy storage abnormal loss value exceeds the preset energy storage abnormal loss threshold, the corresponding detection period is marked as an energy storage risk period; The number of energy storage risk periods in a unit time is obtained and the ratio is calculated with the number of detection periods to obtain the energy storage risk time condition value, and the energy storage abnormal loss values ​​of all detection periods in a unit time are averaged to obtain the energy storage abnormal table value, and the energy storage abnormal loss value with the largest value in a unit time is marked as the energy storage abnormal amplitude value; the energy storage leakage assessment value is obtained by numerically calculating the energy storage risk time condition value, the energy storage abnormal table value and the energy storage abnormal amplitude value. If the energy storage leakage assessment value exceeds the preset energy storage leakage assessment threshold, a high impact signal for energy storage safety is generated.

4. The energy storage charging control system adapted for photovoltaic equipment according to claim 3 is characterized in that: If the energy storage leakage assessment value does not exceed the preset energy storage leakage assessment threshold, the temperature data of several positions in the energy storage device are collected, and the average value of the temperature data of all positions is marked as the energy storage internal temperature value. A rectangular coordinate system is established with time as the X-axis and the energy storage internal temperature value as the Y-axis, and a change curve of the energy storage internal temperature value is drawn in the first quadrant and marked as a gradual change curve, and the starting point of the gradual change curve is located on the Y-axis; In the first quadrant, draw a ray parallel to the X-axis with its endpoint on the Y-axis. Mark the part of the gradient curve above the ray and the area enclosed by the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to obtain the temperature warning performance value. The energy storage safety impact coefficient is obtained by numerically calculating the temperature alarm performance value, vibration analysis value and noise analysis value; if the energy storage safety impact coefficient exceeds the preset energy storage safety impact coefficient threshold, a high energy storage safety impact signal is generated; if the energy storage safety impact coefficient does not exceed the preset energy storage safety impact coefficient threshold, a low energy storage safety impact signal is generated.

5. The energy storage charging control system adapted for photovoltaic equipment according to claim 1 is characterized in that: The energy storage safety impact analysis module is communicatively connected to the energy storage stability analysis module. The energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module. The energy storage stability analysis module analyzes the energy storage stability status of the energy storage device during the detection period, generates an energy storage high stability signal or an energy storage low stability signal through analysis, and sends the energy storage high stability signal or the energy storage low stability signal to the background management end. When the background management end receives the energy storage low stability signal, it issues a corresponding warning.

6. The energy storage charging control system adapted for photovoltaic equipment according to claim 5 is characterized in that: The specific analysis process of the energy storage stability analysis module includes: The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage danger frequency value, and the generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage danger meter time, and the interval between two adjacent groups of energy storage danger meter times is marked as the danger interval value. If the danger interval value does not exceed the preset danger interval threshold, the corresponding danger interval value is marked as the danger interval value; The number of dangerous situation isolation values ​​within the detection period is obtained and marked as dangerous situation isolation values, and the energy storage dangerous frequency value and the dangerous situation isolation value are weighted and summed to obtain the energy storage stability influence coefficient. If the energy storage stability influence coefficient exceeds the preset energy storage stability influence coefficient threshold, an energy storage low stability signal is generated; if the energy storage stability influence coefficient does not exceed the preset energy storage stability influence coefficient threshold, an energy storage high stability signal is generated.

7. The energy storage charging control system adapted for photovoltaic equipment according to claim 5, characterized in that: The energy storage stability analysis module is communicatively connected to the energy storage control analysis module. The energy storage management analysis module obtains all energy storage devices that need to be managed, analyzes the control difficulty of all energy storage devices, and determines whether to generate an energy storage control enhancement signal through analysis, and sends the energy storage control enhancement signal to the background management end.

8. The energy storage charging control system adapted for photovoltaic equipment according to claim 7 is characterized in that: The specific process of analyzing and determining whether to generate an energy storage management enhancement signal is as follows: If the corresponding energy storage device corresponds to the low-stable energy storage signal, the corresponding energy storage device is marked as an abnormal storage device; if the corresponding energy storage device corresponds to the high-stable energy storage signal, the energy storage tracking value of the corresponding energy storage device is obtained through energy storage device tracking analysis, and if the energy storage tracking value exceeds the preset energy storage tracking threshold, the corresponding energy storage device is marked as an abnormal storage device; The number of energy storage devices is obtained and the ratio thereof is calculated with the total number of energy storage devices that need to be supervised to obtain an energy storage detection value. If the energy storage detection value exceeds a preset energy storage detection threshold, an energy storage control enhancement signal is generated.

9. The energy storage charging control system adapted for photovoltaic equipment according to claim 8, characterized in that: The specific analysis process of energy storage device tracking analysis is as follows: The production date of the corresponding energy storage device is collected, and the interval between the current date and the production date is marked as the energy storage duration, and the power storage performance degradation of the corresponding energy storage device is collected and marked as storage performance degradation; The total time that the corresponding energy storage device is in an overcharge state and an over-discharge state is collected, and the total time in the overcharge state and the total time in the over-discharge state are summed up to obtain the abnormal charging and discharging value; the total time that the corresponding energy storage device is in an external risk state in the historical stage is obtained and marked as the external risk time value, and the energy storage tracking value is obtained by numerically calculating the energy storage time, storage degradation, abnormal charging and discharging values ​​and external risk time value.

Images